Constructing input to neural networks for modeling temperature-caused modal variability: Mean temperatures, effective temperatures, and principal components of temperatures-Reference-Cited by-同舟云学术

Constructing input to neural networks for modeling temperature-caused modal variability: Mean temperatures, effective temperatures, and principal components of temperatures

Published:2010-06 Issue:6 Volume:32 Page:1747-1759
ISSN:0141-0296
Container-title:Engineering Structures
language:en
Short-container-title:Engineering Structures

Author:

Zhou H.F.,Ni Y.Q.,Ko J.M.

Publisher

Elsevier BV

Subject

Civil and Structural Engineering

Reference42 articles.

1. Dynamic monitoring as a tool for long span bridges;Roberts,1996

2. Effect of temperature on dynamic system parameters of a highway bridge;Abdel Wahab;Struct Eng Internat,1997

3. Variability of modal parameters measured on the Alamosa Canyon Bridge;Farrar,1997

4. Environmental variability of modal properties;Cornwell;Exp Tech,1999

5. Effects of testing, analysis, damage, and environment on modal parameters;Alampalli;Mech Syst Signal Process,2000

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